Lucene – Quickly add Index and Search Capability

Srividhya UmashankerDecember 27th, 2012Last Updated: December 29th, 2012

1 93 4 minutes read

What is Lucene?

Apache LuceneTM is a high-performance, full-featured text search engine library written entirely in Java. It is a technology suitable for nearly any application that requires full-text search, especially cross-platform.

Lucene can plain text, integers, index PDF, Office Documents. etc.,

How Lucene enables Faster Search?

Lucence creates something called Inverted Index. Normally we map document -> terms in the document. But, Lucene does the reverse. Creates a index term -> list of documents containing the term, which makes it faster to search.

Install Lucene

Maven Dependency

<pre class='brush:xml'><dependency>
 <groupid>org.apache.lucene</groupid>
 <artifactid>lucene-core</artifactid>
 <version>3.0.2</version>
 <type>jar</type>
 <scope>compile</scope>
</dependency>

Download Dependency

Download Lucene from http://lucene.apache.org/ and add the lucene-core.jar in the classpath

How does Lucene Work?

Let’s understand the picture first from bottom – Center. The Raw Text is used to create a Lucene ‘Document’ which is analyzed using the specified Analyzer and Document is added to the index based on the Store, TermVector and Analzed property of the Fields.

Next, the search from top to center. The users specify the query in a text format. The query Object is build based on the query text and the result of the executed query is returned as TopDocs.

Core Lucene Classes

Directory, FSDirectory, RAMDirectory	Directory containing Index File system based index dir Memory based index dir	Directory indexDirectory = FSDirectory.open(new File(‘c://lucene//nodes’));
IndexWriter	Handling writing to index – addDocument, updateDocument, deleteDocuments, merge etc	IndexWriter writer = new IndexWriter(indexDirectory, new StandardAnalyzer(Version.LUCENE_30), new MaxFieldLength(1010101));
IndexSearcher	Search using indexReader – search(query, int)	IndexSearcher searcher = new IndexSearcher(indexDirectory);
Document	DTO used to index and search	Document document = new Document();
Field	Each document contains multiple fields. Has 2 part, name, value.	new Field(‘id’, ‘1’, Store.YES, Index.NOT_ANALYZED)
Term	A word from test. Used in search.2 parts.Field to search and value to search	Term term = new Term(‘id’, ‘1’);
Query	Base of all types of queries – TermQuery, BooleanQuery, PrefixQuery, RangeQuery, WildcardQuery, PhraseQuery etc.	Query query = new TermQuery(term);
Analyzer	Builds tokens from text, and helps in building index terms from text	new StandardAnalyzer()

The Lucene Directory

Directory – is the data space on which lucene operates. It can be a File System or a Memory.

Below are the often used Directory

Analyzers

The job of analyzing text into tokens or keywords to be searched. There are few default Analyzers provided by Lucene. The choice of Analyzer defined how the indexed text is tokenized and searched.

Below are some standard analyzers.

Example – How analyzers work on sample text

Properties that define Field indexing

Store – Should the Field be stored to retrieve in the future
ANALYZED – Should the contents be split into tokens
TermVECTOR – Term based details to be stored or not

Store :

Should the field be stored to retreive later

STORE.YES	Store the value, can be retrieved later from the index
STORE.NO	Don’t store. Used along with Index.ANALYZED. When token are used only for search

Analyzed:

How to analyze the text

Index.ANALYZED	Break the text into tokens, index each token make them searchable
Index.NOT_ANALYZED	Index the whole text as a single token, but don’t analyze (split them)
Index.ANALYZED_NO_NORMS	Same as ANALYZED, but does not store norms
Index.NOT_ANALYZED_NO_NORMS	Same as NOT_ANALYZED, but without norms
Index.NO	Don’t make this field searchable completely

Term Vector

Need Term details for similar, highlight etc.

TermVector.YES	Record UNIQUE TERMS + COUNTS + NO POSITIONS + NO OFFSETSin each document
TermVector.WITH_POSITIONS	Record UNIQUE TERMS + COUNTS + POSITIONS + NO OFFSETSin each document
TermVector.WITH_OFFSETS	Record UNIQUE TERMS + COUNTS + NO POSITIONS + OFFSETSin each document
TermVector.WITH_POSITIONS_OFFSETS	Record UNIQUE TERMS + COUNTS + POSITIONS + OFFSETSin each document
TermVector.NO	Don’t record term vector information

Example of Creating Index

IndexWriter writer = new IndexWriter(indexDirectory, new StandardAnalyzer(Version.LUCENE_30), true,MaxFieldLength.UNLIMITED);

Document document = new Document();
document.add(new Field('id', '1', Store.YES, Index.NOT_ANALYZED));
document.add(new Field('name', 'user1', Store.YES, Index.NOT_ANALYZED));
document.add(new Field('age', '20', Store.YES, Index.NOT_ANALYZED));
writer.addDocument(document);

Example of Updating Index

IndexWriter writer = new IndexWriter(indexDirectory, new StandardAnalyzer(Version.LUCENE_30), true,MaxFieldLength.UNLIMITED);
 
Document document = new Document();
document.add(new Field("id", "1", Store.YES, Index.NOT_ANALYZED));
document.add(new Field("name", "user1", Store.YES, Index.NOT_ANALYZED));
document.add(new Field("age", "20", Store.YES, Index.NOT_ANALYZED));
writer.addDocument(document);

Example of Deleting Index

IndexWriter writer = new IndexWriter(indexDirectory, new StandardAnalyzer(Version.LUCENE_30), MaxFieldLength.UNLIMITED);

Term term = new Term('id', '1');
writer.deleteDocuments(term);

Searching an Index : The users specify the query in a text format. The query Object is built based on the query text, analyzed and the result of the executed query is returned as TopDocs.
Queries are the main input for the search.

TermQuery
BooleanQuery	AND or not ( combine multiple queries)
PrefixQuery	Starts with
WildcardQuery	? And * – * not allowed in the beginning
PhraseQuery	Exact phrase
RangeQuery	Term range or numeric range
FuzzyQuery	Similar words search

Sample Queries

Example on Search:

IndexSearcher searcher = new IndexSearcher(indexDirectory);
Term term = new Term('id', '1');
Query query = new TermQuery(term);
TopDocs docs = searcher.search(query, 3);
for (int i = 1; i <= docs.totalHits; i++)
{
     System.out.println(searcher.doc(i));
}

Lucene Diagnostic Tools:

Luke – http://code.google.com/p/luke/
Luke is a handy development and diagnostic tool, which accesses already existing Lucene indexes and allows you to display and modify their content in several ways:
Limo – http://limo.sourceforge.net/
The idea is to have a small tool, running as a web application, that gives basic information about indexes used by the Lucene search engine